Pandas的View和Copy

View vs Copy

记一下这两天发现的问题.

对frame或者series进行操做常常不知道会不会对原数据操做改变，并且与遇到SetingWithView的警告，作了下实验。

Series

太长不看：

1. slice是view (和numpy保持一致)
2. loc进行boolean筛选后的新Series是copy
3. copy()方法后的是copy
4. Series.T 转置以后的Series也是view (和numpy保持一致)

DataFrame

太长不看：

1. 对index或者column 进行slice是view
2. loc单选一个index 或一个column是view
3. loc进行boolean筛选是copy
4. loc进行[ ] or () 选择是copy
5. 使用1.2.3.4对frame同时进行index和column的操做，当且仅当index和column都是结果为view操做时，返回的frame或series才是view，其余是copy

详细

太忙了，下次再写

测试code

import numpy as np
import pandas as pd

a=pd.Series([1,2,3,4,5,6,7,8,9])
aid(a.values),aid(a.copy().values),aid(a[0:2].values),aid(a.loc[a%2==0].values),aid(a.T.values)

A=pd.DataFrame([[0,0,0],[0,0,0],[0,0,0]],columns=['c1','c2','c3'])
B=A['c1']
C=A.loc[:,'c1']
C1=A.loc[:,['c1']]
# D=A.loc[:,'c1':'c2']
E=A.T
aid(A['c1'].values),aid(B.values),aid(C.values),aid(C1['c1'].values),aid(E.loc[].values) #,aid(D['c1'].values)

# another testing
A=pd.DataFrame([[0,0,0],[0,0,0],[0,0,0]],columns=['c1','c2','c3'])


B=A.loc[0]
C=A.loc[0:1]
C1=A.loc[0:1,['c1','c2']]
C2=A.loc[0:1,'c1']
D=A.loc[A.index<2]


A.at[0,'c1']=1

print('A......')
print(A)
print('B......')
print(B)
print('C......')
print(C)
print('C1......')
print(C1)
print('C2......')
print(C2)
print('D......')
print(D)